山区双车道公路货车移动遮断小客车跟驰风险预测模型

doi:10.13229/j.cnki.jdxbgxb.20220744

Abstract

Abstract:

Taking the typical mountainous two-lane highway bend and straight road as the research object， based on traffic trajectory data extracted by UAV aerial video， the risk prediction model of passenger car following under the movement interruption of truck was constructed by the light gradient boosting machine algorithm （LGBM）. The support vector machine （SVM） and random forest machine （RF） were used to verify the validity of the model， and the risk mechanism of the key characteristic parameters of the model was analyzed. The experimental results show that the accuracy of the risk prediction model based on the LGBM algorithm is 96.9%， which is superior. The speed difference and the following distance are the key characteristic parameters of the model， and the single factor importance on the straight road is greater. Compared with the curve， the dangerous driving behavior of straight road section is prominent， and the unstable following characteristics such as large lateral offset are obvious； the results of the model interpreter show that when the speed difference is less than 0.5 m/s and the car-following distance is greater than 40 m， it is a safe car-following state.

Key words: traffic and transportation safety engineering, risk prediction of car-following, LGBM algorithm, truck movement interruption, mountain two-lane highway

CLC Number:

U491.31

Xiao-feng JI,Ying-hao XU,Yong-ming PU,Jing-jing HAO,Wen-wen QIN. Risk prediction model of passenger car following behavior under truck movement interruption of two-lane highway in mountainous area[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1323-1331.

Figures/Tables 16

Table 1

Conflict risk classification rules"

风险等级	含义	冲突可能性划分规则
四级	车辆间具有严重冲突，存在较大碰撞风险	碰撞时间<1%分位值
三级	车辆间同时具有严重和一般冲突，存在潜在碰撞风险	1%分位值 $≤$ 碰撞时间<5%分位值
二级	车辆间存在一般冲突，可及时避让	5%分位值 $≤$ 碰撞时间<85%分位值
一级	车辆间存在轻微冲突，可安全行驶	85%分位值 $≤$ 碰撞时间

Table 1

Table 2

Model evaluation indicators"

指标（符号）	含义	公式
查准率（P）	预测样本有多少概率真正的正样本	$P = T P T P + F P$
查全率（R）	有多少比例正样本被正确预测	$R = T P T P + F N$
F1值（F1）	P和R的综合指标	$F 1 = 2 × P × R P + R$

Table 2

Fig. 1

Fig. 2

Table 3

Single sample t-test of key characteristic parameters"

变量（符号）	单位	路段	均值	标准差	差分的95%置信区间		Sig.（双侧）
变量（符号）	单位	路段	均值	标准差	下限	上限	Sig.（双侧）
车头时距（ $T i j$ ）	$s$	直道	2.91	1.43	2.86	2.96	0
车头时距（ $T i j$ ）	$s$	弯道	3.53	1.89	3.47	3.59	0
跟驰间距（ $S i j$ ）	$m$	直道	21.24	19.05	20.65	21.84	0
跟驰间距（ $S i j$ ）	$m$	弯道	24.01	13.80	23.55	24.47	0
跟驰速度差（ $V i j$ ）	$m / s$	直道	0.88	2.19	0.81	0.96	0.01
跟驰速度差（ $V i j$ ）	$m / s$	弯道	0.15	2.19	0.08	0.22	0
小客车相对偏移（ $L$ ）	$m$	直道	-0.43	0.21	-0.65	-0.22	0
小客车相对偏移（ $L$ ）	$m$	弯道	-0.28	0.11	-0.32	-0.23	0
小客车速度（ $V j$ ）	$m / s$	直道	39.55	14.60	39.07	40.04	0
小客车速度（ $V j$ ）	$m / s$	弯道	35.74	18.40	35.16	36.31	0

Table 3

Fig.3

Fig.4

Fig.5

Fig.6

Table 4

Risk level threshold"

风险等级

TTC/s

四级

（0，2.9）

三级

二级

一级

（2.9，5.9）

（5.9，8.3）

（8.3， $+ ∞$ ）

Table 4

Fig.7

Table 5

Key characteristic parameters"

类别	参数	物理量	计算方式
货车	货车速度	$V i$ /（km·h^-1）	/
	货车加速度	$A i$ /（m·s^-2）	/
	货车轨迹曲率	$C u r$	/
	货车长度	$H$ /m	/
小客车	小客车速度	$V j$ /（km·h^-1）	/
	小客车加速度	$A j$ /（m·s^-2）	/
	小客车相对偏移	$L$ /m	$x i - x j, 向东 x j - x i, 向西$
交通流	车头时距	$T i j$ /s	$T i j = (S i j + H) / V j$
	跟驰间距	$S i j$ /m	$S i j = (x i - x j) 2 + (y i - y j) 2 - H$
	速度差	$V i j$ /（m·s^-1）	$V i - V j$

Table 5

Fig.8

Table 6

Fig.9

Fig.10

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预测模型	Precision	Recall	F1值
LGBM	0.969	0.965	0.966
SVM	0.914	0.907	0.907
RF	0.965	0.963	0.963

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Risk prediction model of passenger car following behavior under truck movement interruption of two-lane highway in mountainous area

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